One incentive to joining in Intro to Parallel Programming now is that students who are among the first to complete its units and problem sets have an opportunity to win a Kepler GPU. See the Udacity blog for details of this promotion sponsored by NVIDIA, as well as one from Amazon Web Services for GPU compute instances.

Udacity now has a total of 15 computer science courses in its catalog You can also enrol in any of its previous courses and complete them at you own pace, earning a certificate as soon as you complete the course.

Coursera also offers some of its courses on a self-study basis and Compilers, taught by Stanford University Professor of Computer Science, Alex Aiken, is one of them. However, if you prefer the timetabled version, it next starts on February 11 and lasts for 11 weeks. This class looks at the major ideas used in the implementation of programming language compilers, including lexical analysis, parsing, syntax-directed translation, abstract syntax trees, types and type checking, intermediate languages, dataflow analysis, program optimization, code generation, and runtime systems. An optional course project is to write a complete compiler for COOL, the Classroom Object Oriented Language.

Algorithms Part 1, a 6-week course taught by Robert Sedewick and Kevin Wayne of Princeton University for students familiar with programming in Java started on February 4. It focuses on elementary data structures, sorting, and searching and there's a follow-on course that looks at graph and string-processing algorithms.

Later in the month Coursera offers Natural Language Processing, an online version of course currently taught by Professor Michael Collins at Columbia University, and previously taught at MIT. It covers mathematical and computational models of language, and the application of these models to key problems in natural language processing and has a focus on machine learning methods.

A course on Quantum Mechanics and Quantum Computation, intended to be accessible to computer science majors who have a strong background in linear algebra stats its second, and much revised, presentation on the edX platform on February 11.

Another edX re-run started on February 4 - also saw the MIT's 6.00x: Introduction to Computer Science and Programming, taught by Professor Eric Grimson. Having sampled this already I can recommend it as a good way for beginners to learn Python if you can devote the required number of hours - estimated at 12 per week for 16 weeks.

The first in a series of MOOCs on complex systems science offered in its new Complexity Explorer platform by the Santa Fe Institute also started on February 4. Taught by Melanie Mitchell, Professor of Computer Science at Portland State University, Introduction to Complexity is an 11-week course which takes a hands-on approach to understanding complex systems. It has no prerequisites and suggests a workload of 3-6 hours per week, some of it using NetLogo, a free multi-agent programmable modeling environment.

The topics covered include dynamics, chaos, fractals, information theory, self-organization, agent-based modeling, and networks and the way they fit together to help explain how complexity arises and evolves in nature, society, and technology. If you enroll in the course and complete homeworks and a final exam you can earn a certificate and enrolled participants can join in forum discussions. Otherwise you can just watch the videos, complete with quizzes and they will be available indefinitely.

If you have time to spare the current crop of MOOCs provide a good opportunity to expand your knowledge. If you know of courses starting in February that should be in this list, use the comments to let us know.